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A few-shot RE method based on a prototype network, which constructs relation prototypes by relying on the support set to assign labels to query samples, inherently leverages the symmetry between support and query processing. Although these methods have achieved remarkable results, they still face challenges such as the misjudging of noisy samples or outliers, as well as distinguishing semantic similarity relations. To address the aforementioned challenges, we propose a novel semantic enhanced prototype network, which can integrate the semantic information of relations more effectively to promote more expressive representations of instances and relation prototypes, so as to improve the performance of the few-shot RE. Firstly, we design a prompt encoder to uniformly process different prompt templates for instance and relation information, and then utilize the powerful semantic understanding and generation capabilities of large language models (LLMs) to obtain precise semantic representations of instances, their prototypes, and conceptual prototypes. Secondly, graph attention learning techniques are introduced to effectively extract specific-relation features between conceptual prototypes and isomorphic instances while maintaining structural symmetry. Meanwhile, a prototype-level contrastive learning strategy with bidirectional feature symmetry is proposed to predict query instances by integrating the interpretable features of conceptual prototypes and the intra-class shared features captured by instance prototypes. In addition, a clustering loss function was designed to guide the model to learn a discriminative metric space with improved relational symmetry, effectively improving the accuracy of the model\u2019s relationship recognition. Finally, the experimental results on the FewRel1.0 and FewRel2.0 datasets show that the proposed approach delivers improved performance compared to existing advanced models in the task of few-shot RE.<\/jats:p>","DOI":"10.3390\/sym17101673","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T08:05:36Z","timestamp":1759824336000},"page":"1673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prototype-Enhanced Few-Shot Relation Extraction Method Based on Cluster Loss Optimization"],"prefix":"10.3390","volume":"17","author":[{"given":"Shenyi","family":"Qian","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Bowen","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information and Managment Science, Henan Agricultural University, Zhengzhou 450046, China"}]},{"given":"Songhe","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Tong","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information and Managment Science, Henan Agricultural University, Zhengzhou 450046, China"}]},{"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information and Managment Science, Henan Agricultural University, Zhengzhou 450046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3677-4636","authenticated-orcid":false,"given":"Daiyi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Yifan","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Yibing","family":"Chen","sequence":"additional","affiliation":[{"name":"International Business School, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"}]},{"given":"Yuheng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Nanyang Technological University, 50 Nanyang Road, Singapore 639798, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3674501","article-title":"A comprehensive survey on relation extraction: Recent advances and new frontiers","volume":"56","author":"Zhao","year":"2024","journal-title":"ACM Comput. 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